25,950 research outputs found
Vessel tractography using an intensity based tensor model with branch detection
In this paper, we present a tubular structure seg- mentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmen- tation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient Computed Tomography Angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert
Image informatics strategies for deciphering neuronal network connectivity
Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies
Ten simple rules for reporting voxel-based morphometry studies
Voxel-based morphometry [Ashburner, J. and Friston, K.J., 2000. Voxel-based morphometry—the methods. NeuroImage 11(6 Pt 1), 805–821] is a commonly used tool for studying patterns of brain change in development or disease and neuroanatomical correlates of subject characteristics. In performing a VBM study, many methodological options are available; if the study is to be easily interpretable and repeatable, the processing steps and decisions must be clearly described. Similarly, unusual methods and parameter choices should be justified in order to aid readers in judging the importance of such options or in comparing the work with other studies. This editorial suggests core principles that should be followed and information that should be included when reporting a VBM study in order to make it transparent, replicable and useful
Airborne LiDAR for DEM generation: some critical issues
Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of
LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for
DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage
and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity
characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented
Direct exoplanet detection and characterization using the ANDROMEDA method: Performance on VLT/NaCo data
Context. The direct detection of exoplanets with high-contrast imaging
requires advanced data processing methods to disentangle potential planetary
signals from bright quasi-static speckles. Among them, angular differential
imaging (ADI) permits potential planetary signals with a known rotation rate to
be separated from instrumental speckles that are either statics or slowly
variable. The method presented in this paper, called ANDROMEDA for ANgular
Differential OptiMal Exoplanet Detection Algorithm is based on a maximum
likelihood approach to ADI and is used to estimate the position and the flux of
any point source present in the field of view. Aims. In order to optimize and
experimentally validate this previously proposed method, we applied ANDROMEDA
to real VLT/NaCo data. In addition to its pure detection capability, we
investigated the possibility of defining simple and efficient criteria for
automatic point source extraction able to support the processing of large
surveys. Methods. To assess the performance of the method, we applied ANDROMEDA
on VLT/NaCo data of TYC-8979-1683-1 which is surrounded by numerous bright
stars and on which we added synthetic planets of known position and flux in the
field. In order to accommodate the real data properties, it was necessary to
develop additional pre-processing and post-processing steps to the initially
proposed algorithm. We then investigated its skill in the challenging case of a
well-known target, Pictoris, whose companion is close to the detection
limit and we compared our results to those obtained by another method based on
principal component analysis (PCA). Results. Application on VLT/NaCo data
demonstrates the ability of ANDROMEDA to automatically detect and characterize
point sources present in the image field. We end up with a robust method
bringing consistent results with a sensitivity similar to the recently
published algorithms, with only two parameters to be fine tuned. Moreover, the
companion flux estimates are not biased by the algorithm parameters and do not
require a posteriori corrections. Conclusions. ANDROMEDA is an attractive
alternative to current standard image processing methods that can be readily
applied to on-sky data
Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping
The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability
Fusion based analysis of ophthalmologic image data
summary:The paper presents an overview of image analysis activities of the Brno DAR group in the medical application area of retinal imaging. Particularly, illumination correction and SNR enhancement by registered averaging as preprocessing steps are briefly described; further mono- and multimodal registration methods developed for specific types of ophthalmological images, and methods for segmentation of optical disc, retinal vessel tree and autofluorescence areas are presented. Finally, the designed methods for neural fibre layer detection and evaluation on retinal images, utilising different combined texture analysis approaches and several types of classifiers, are shown. The results in all the areas are shortly commented on at the respective sections. In order to emphasise methodological aspects, the methods and results are ordered according to consequential phases of processing rather then divided according to individual medical applications
Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy
Automated label-free quantitative imaging of biological samples can greatly
benefit high throughput diseases diagnosis. Digital holographic microscopy
(DHM) is a powerful quantitative label-free imaging tool that retrieves
structural details of cellular samples non-invasively. In off-axis DHM, a
proper spatial filtering window in Fourier space is crucial to the quality of
reconstructed phase image. Here we describe a region-recognition approach that
combines shape recognition with an iterative thresholding to extracts the
optimal shape of frequency components. The region recognition technique offers
fully automated adaptive filtering that can operate with a variety of samples
and imaging conditions. When imaging through optically scattering biological
hydrogel matrix, the technique surpasses previous histogram thresholding
techniques without requiring any manual intervention. Finally, we automate the
extraction of the statistical difference of optical height between malaria
parasite infected and uninfected red blood cells. The method described here
pave way to greater autonomy in automated DHM imaging for imaging live cell in
thick cell cultures
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